Improving absorbed dose estimation for treatment planning in Molecular Radiotherapy

Molecular Radiotherapy (MRT) is a rapidly growing cancer treatment modality where molecules that bind to cancerous cells are labelled with a radionuclide and injected into the patient for targeted delivery of radiation. Multimodality imaging using CT and nuclear imaging (SPECT/PET) can be performed to quantify absorbed doses to the tumours and organs at risk. Nevertheless, personalised treatments have not yet made it into routine clinical use. This is partly due to a lack of standardisation and knowledge on the uncertainties in dosimetry calculations as well as increased resources needed, leading to a lack of evidence from large randomised clinical trials.

Machine learning techniques are under investigation for nuclear medicine dosimetry but have not yet been implemented clinically due to the lack of validation and knowledge on their potential benefits. In particular, deep learning models have been proposed as a way to increase the speed of the absorbed dose calculation step as well as decreasing the need for imaging resources.

The aim is to contribute towards the implementation of personalised treatment planning into clinical practice by characterising and improving accuracy and uncertainty of dosimetry calculations from SPECT and CT images. The student will compare conventional methods for absorbed dose map generation with state-of-the-art Machine Learning methods. Sensitivity to acquisition protocol and reconstruction method will be investigated as well, with a view to optimise and simplify protocols (e.g. towards single time point dosimetry).

The project will include the development and validation of a simulation framework for generating a realistic ground truth dataset using open source software GATE/ STIR / OpenDose / Dositest, based on existing clinical data from theragnostic studies at UCLH. Some experiments using phantoms will have to be performed at UCLH and NPL.

In line with EPSRC strategy and research areas the project will assist in the creation of safer and more targeted patient specific radiation treatments. Novel imaging technologies will be explored including different imaging reconstruction techniques in multi-modal imaging systems and Machine Learning methods. Optimisation and simplification of protocols will accelerate translation of novel radiation therapies in real world clinical applications.

This project is a collaboration between the Institute of Nuclear Medicine (INM), and the nuclear medicine group of the National Physical Laboratory. The student will be part of the UCL i4health Centre for Doctoral Training (CDT) and the postgraduate institute (PGI) at NPL, and will benefit from a wide range of activities and opportunities. The student will be primarily located at INM, near the UCL Bloomsbury Campus. Imaging facilities include SPECT-CT, PET-CT and PET-MRI scanners. A substantial proportion of the student's time will be spent at the NPL Teddington Campus for collaboration and experiments. The supervisory team will include Prof Kris Thielemans (UCL), Dr Sarah McQuaid (UCLH) and Dr Ana Denis-Bacelar (NPL).

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.